System and method for designing simulant composition

ABSTRACT

In one embodiment, a method for identifying and forming a simulant includes identifying a composition, identifying a plurality of ingredients, the simulant being a combination of the ingredients, identifying for evaluation one or more metrics of the simulant, determining proportions of each of the ingredients by optimizing a quadratic function based on the one or more metrics of the simulant, rendering, via a GUI, a 3D plot that depicts the metrics and a target point specified by the target values of the composition, when the target point is contained within a convex set defined by the identified ingredients, outputting the determined proportions of each of the identified ingredients and, otherwise, receiving user input to adjust the convex set by user selecting and moving the data points of the 3D plot to modify the metrics of the simulant to produce a new 3D plot, and identifying alternative ingredients and/or alternative proportions.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a continuation of and claims the benefit of priorityfrom U.S. patent application Ser. No. 15/246,729, filed on Aug. 25,2016, entitled SYSTEMS AND METHODOLOGIES FOR DESIGNING SIMULANTCOMPOUNDS, the disclosure of which is incorporated by reference in itsentirety.

STATEMENT OF GOVERNMENT RIGHTS

This invention was made with government support under Contract No.HSHQDC-04-J-00001 awarded by the United States Department of HomelandSecurity. The government has certain rights in the invention.

BACKGROUND

Simulants are used in X-ray based Explosive Detection Systems (EDS) andX-ray or millimeter wave (MMW) based Advanced Imaging Technology (AIT)portals as a safe surrogate for training and testing purposes.

Explosive simulants are commonly used to field test various explosivedetection systems and to train operators of such equipment. Explosivesimulants are largely designed for X-ray imaging and explosive detectionsystem (EDS) platforms, where an explosive's X-ray parameters arematched. The EDS is commonly used to identify explosives in luggage.However, the EDS platforms generally do not focus on aspects such asedge effects, compressibility, or flexibility of an explosive. Whilematching simulant and explosive morphology in a very general sense ispossible, little has been done to determine the relevance of these typesof material properties to EDS detection algorithms, or to measure andvalidate them. While these properties of simulants have not beencritical in simulant development for EDS, they are much more relevantfor simulants used for a different type of explosive detection systemsuch as an Advanced Imaging Technology (AIT) portal.

AIT portals use detection algorithms that rely heavily on anomalydetection algorithms, and therefore it is imperative that simulantsbehave in a manner similar to that of the actual explosive. As such,when developing AIT portals, there is an ever-increasing need forsimulants to match the morphological properties of explosives in orderfor simulants to become indistinguishable from live explosives.

AIT portals based on backscatter X-ray or millimeter wave (MMW) scanningtechnologies require validated simulants for testing and development.When used in MMW based AIT portals, simulants and explosives withsimilar dielectric properties produce similar grayscale responses.Regardless of the MMW dielectric response or the X-ray backscatterresponse, the AIT algorithm for threat detection is focused on anomalyidentification, as opposed to material identification. The properties ofthe explosive or simulant that affect the image will be important; forMMW technology, the dielectric properties and the thickness determinethe average reflectance, but the shape also affects the image. Thisshape, which includes the shape of the outer packaging as well as thethickness of the explosive or simulant material within the packaging,can be affected by the morphological properties.

Conventional methods for designing a new simulant that matches aparticular explosive threat requires a trial and error process that maytake many iterative steps, resulting in a loss of time and efficiency.Therefore, in view of the foregoing, there is a need for improvedsystems and methods for designing simulants.

SUMMARY

The present disclosure relates to a method for identifying and forming asimulant. The method may include identifying a compound using processingcircuitry, and identifying a plurality of ingredients. The simulant canbe a combination of the plurality of ingredients. In addition, theprocessing circuitry may identify one or more metrics for the simulantand determine proportions of each of the plurality of ingredients byoptimizing a quadratic function based on the one or more metrics, andoutput the proportions of each of the plurality of ingredients.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of a system for identifying a simulantaccording to one example;

FIG. 2 is a flow chart illustrating a method for determining ingredientsof the simulant according to one example;

FIG. 3 is a flow chart illustrating a method for determining ingredientsof the simulant according to one example; and

FIG. 4 is a schematic that shows a graphical user interface according toone example.

DETAILED DESCRIPTION

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout several views, the followingdescription relates to a system and associated methodology foridentifying a simulant that matches characteristic properties of acompound (e.g., an explosive detector response, a flavor, a texture).

Simulants are needed for both training and testing explosive detectionsystems and advanced imaging technology portals. The simulants are usedin place of live explosive threats in locations where live explosivescannot be used due to concerns of public safety. Although it is possiblefor a single ingredient to be a satisfactory simulant, simulants areusually physical mixtures of two or more non-explosive components(ingredients). Simulants are manufactured to produce the same detectorresponse as live threats but as technology improves, more measurableproperties may be needed for a given simulant to match a specificthreat.

In one example, the system and associated methodology described hereinmodel the characteristic properties of explosive threats for matchingcombinations of ingredients that generate a formula for making safeexplosive simulants. Simulants are used as a safe surrogate for trainingand testing for threat detection in X-ray based explosive detectionsystems (EDS) and in X-ray based or millimeter wave (MMW) based advancedimaging technology (AIT) portals. Simulants are designed to produce thesame detector response as explosive threats, but may also be designed tohave the same properties as a threat as might be recognized by a humanscreener, such as color or texture.

Properties used as metrics for comparison between simulants andexplosive threats are measurable or computable properties that may scalewith weight percent, and can be added to a database of threats andsimulant ingredients. When designing simulants, the objective is tomatch such metrics of the simulant to those of the actual explosivethreat. Density and effective atomic number are two such properties thatmay be used for generating explosive simulants. Other physicalattributes like color, particle size, and texture may also be used. Theuser may select a threat, simulant ingredients, and desired propertiesfor optimization through, for example, a graphical user interface (GUI).

The methodology described herein provides an efficient anduser-controlled way to design explosive simulants via matching tabulatedphysical properties of simulants to those of explosive threats. Theclaimed methodology can also be used with dielectric properties leadingto simulant formulas that match both X-ray and MMW properties oftargeted threat materials. The methodology may also be used to designsimulants for other forms of contraband apart from explosive threats,such as explosive precursors, oxidizers, illegal drugs, or eveninnocuous materials, such as food products, by creating simulantmixtures that match (mimic) a set of targeted properties of threats andother forms of contraband.

The physical properties used as metrics to predict the properties thatthe new simulant would need to possess can be easily substituted. Asdescribed further below, the user may select one or more such metrics.The degree of matching (of a simulant to an explosive threat) can alsobe easily changed by selecting an optimization based on as many metricsas desired. The method described herein for improving the simulantdesign process optimizes the proportions of selected inert ingredientswith calculated mixture percentages and returns a zero weight forselections that are not needed in the formula.

The methodology described herein allows a user to easily adjust from avery restrictive match of a given simulant's physical properties to aless restrictive match of a specific explosive threat if no viablesolution is found. A plurality of metrics (properties) may be used. Theplurality of metrics may include, but are not limited to, density,effective atomic number (Z-effective or Z_(eff)), mass attenuationcoefficient (MAC), electron density, chemical compound ratio (e.g.,Nitrogen-Oxygen to Carbon-Hydrogen ratio), dielectric constant, andmillimeter wave reflectivity. The optimization may include one or moresuch metrics, or the reduction of the number of metrics to a limitednumber of properties to form a least restrictive match. For example, theoptimization can be reduced to just one metric (e.g., density) as theleast restrictive match. The methodology may also be extended to includeadditional metrics with no limitation on how many metrics are attemptedto match a simulant to a specific explosive threat.

FIG. 1 is a schematic diagram of a system for identifying ingredientsthat comprise a simulant in order to provide a representation of acompound, which may be an explosive threat, according to one example.The system may include a computer 100, a network 102, a compounddatabase 104, and an ingredient database 106. In one example, thecompound database 104 and the ingredient database 106 may be cloud-baseddatabases. The computer 100 may connect via communication circuitrythrough the network 102 with the compound database 104 and theingredient database 106.

The compound database 104 includes a list of compounds, which may bethreats. For each compound, the compound database 104 may store aplurality of metrics. For example, when the compound is an explosive,the plurality of metrics may include density, Z_(eff), MAC, electrondensity, elemental ratios, and dielectric properties. The list ofcompounds may include explosive threats having various physical forms,including solids and powders (e.g., military explosives, aluminizedexplosives, trinitrotoluene (TNT), black powder, smokeless powder, orhomemade explosives such as triacetone triperoxide (TATP)).

The ingredient database 106 may include a list of components that may beused to fabricate the simulant. For each component, the ingredientdatabase 106 may store a plurality of metrics. In one example, computer100 may receive a user input. The user input may include theidentification of a compound, one or more ingredients, and a set of theplurality of metrics to be optimized. The system may output weightpercentages of the one or more ingredients that mimic the plurality ofmetrics of the compound.

The computer 100 may include a CPU and a tangibly embodiedcomputer-readable storage medium. The computer-readable storage mediummay store instructions that, when executed by the CPU, perform steps inaccordance with the disclosed embodiments. The databases 104 and 106 ofthe system may be implemented in the memory of the computer 100, or maybe standalone databases as depicted in FIG. 1. Further, in someembodiments, the system may be implemented as an application that may bedownloaded on a mobile device or a handheld computer.

The network 102 may include the Internet or any other network capable ofcommunicating data between devices. Suitable networks can include orinterface with any one or more of a local intranet, a PAN (Personal AreaNetwork), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN(Metropolitan Area Network), a VPN (Virtual Private Network), or a SAN(storage area network). Furthermore, communications may also includelinks to any of a variety of wireless networks, including WAP (WirelessApplication Protocol), GPRS (General Packet Radio Service), GSM (Globalsystem for Mobile Communication), CDMA (Code Division Multiple Access),TDMA (Time Division Multiple Access), cellular phone networks, GPS(Global Positioning System), CDPD (Cellular digit packet data),Bluetooth radio, or an IEEE 802.11 based radio frequency.

The CPU may minimize a quadratic function of three or more ingredients(of N-dimensions) with their weights subject to constraints. Thedimension represents how many physical properties (metrics) are used inthe optimization to match the formula to the compound (e.g., threat).The constraints for the weights are positivity (e.g., zero or positiveweights) or normalization (e.g., all weights add to a predeterminednumber, for example, one). A GUI allows a user to perform variousfunctions for formation of simulants, including selecting ingredients, aparticular threat, and what properties to optimize for matching theformula to the selected threat. The results can be visualized with a3-dimensional plot that can be rotated, an exemplary GUI of which isshown in FIG. 4. The GUI can also display the results of theoptimization in the form of the rotatable 3-dimensional plot.

FIG. 2 is a flow chart illustrating a method for formation of simulantsthat are designed to mimic threat compounds or materials, including thedetermination of the combination of ingredients that comprise thesimulant, according to one example. The computational modeling tooldescribed herein can calculate weight percent of ingredients that map toa specific point (target) representing the explosive threat.

At step S200, the CPU may identify a compound based on a user input. Forexample, the CPU may receive a user request indicating a compound. Thecompound may be a threat. The threat may be a combination of one or moreexisting materials (e.g., 59.6% RDX, 39.4% TNT, and 1% Paraffin wax).

At step S202, the CPU may identify a plurality of ingredients(components) to comprise the simulant design, in order to mimic theproperties of the compound. In one example, the CPU identifies theplurality of ingredients based on the user input. Three or moreingredients may be identified, but the number of ingredients in thesolution may be less (i.e., one or more ingredients may have zeroweights).

At step S204, the CPU may identify one or more metrics. In one example,the CPU identifies the one or more metrics based on the user input. Inother examples, the one or more metrics may be predetermined as afunction of the compound type.

At step S206, the CPU may check to see whether the compound (e.g.,threat) is a combination of existing materials before the optimizationprocess of step S208 occurs. In response to the determination that thecompound is a combination of existing materials, the CPU may determinethe metrics values (e.g., density, Z_(eff), MAC, etc.) of the compoundbased on the metrics values of each material. In addition, the CPU mayuse the elemental composition of the threats and ingredients as well asthe NIST XCOM (National Institute of Standards and Technology Standardreference database). The CPU may connect via the network 102 to the NISTXCOM database to download and/or update metrics values associated withthe compound and/or materials forming the compound. In performing thisstep, the list of potential ingredients may be further restricted as away of pre-optimizing the simulant design.

At step S208, the CPU determines the weights, which may be percentagesout of 100% or proportions of a number such as 1, of each of theplurality of ingredients identified at step S202 based on anoptimization algorithm. The algorithm for optimization can be, forexample, a quadratic programming technique that minimizes the totalsquared deviation of the weights from their mean; however, otheroptimization techniques may be utilized. In one example, the weightproportions or percentages (e.g. 0.47 or 5%, respectively) may be addedtogether to form a predetermined number (e.g., 1 or 100% depending onwhether weight proportions or percentages are used). In addition, theweights may be positive numbers (greater than or equal to zero). Asolution exists if the N-dimensional target point (threat) lies within aconvex set (convex hull) defined by the selected N-dimensionalingredient points. In one example, the CPU may minimize the quadraticfunction based on an interior point method as would be understood by oneof ordinary skill in the art.

At step S210, the CPU may output the weights of the ingredients to theuser. For example, the CPU may generate a three dimensional plot thatdisplays the metrics of the simulant and the compound. In addition, theCPU may output the weights to an external device via the network 102.

At step S212, the simulant is formed based on the weights of theplurality of ingredients determined at step S208.

In one example, if the target point (threat or compound) does not liewithin the convex set defined by the selected simulant ingredients, theuser can then select different ingredients that do define a regionaround the threat and/or reduce the number of properties (a lessrestrictive match between the metrics of the simulant and those of thethreat). Adding simulant ingredients may result in a better match, i.e.a simulant formula that is a better representation of the selectedcompound that is a threat. The more extensive the ingredient libraryfile, the easier it is to find a match. However, in some cases theoptimization may need to be made less restrictive (i.e., reduce thenumber of metrics that were used).

FIG. 3 is a flow chart illustrating a method for formation of simulantsto mimic compounds, including the determination of the combination ofingredients that comprise the simulant and the validation of thesimulant, according to one example. At step S300, the CPU may identify acompound, which may be a threat that the simulant is meant to reproduce,based on a user input. For example, the CPU may receive a user requestindicating a compound. At step S302, the CPU may identify a plurality ofingredients (components) of the simulant. In one example, the CPUidentifies the plurality of ingredients based on the user input. At stepS304, the CPU may identify one or more metrics. In one example, the CPUidentifies the one or more metrics based on the user input. In otherexamples, the one or more metrics may be predetermined.

At step S306, in response to determining that the threat is acombination of existing materials, the CPU determines the metrics valuesof the compound based on the metrics values of each material. At stepS308, the CPU determines the weights of each of the plurality ofingredients identified at step S302 as described previously with respectto FIG. 2.

At step S310, the CPU may check to see whether each metric of thesimulant matches within a predetermined threshold with the correspondingmetric of the compound. In response to determining, that the simulantmatches each of the metrics, then the process goes to step S312. Inresponse to determining, that the simulant does not match the compound,the flow of the method returns to step S302. At step S302, the user maybe presented with the option to select more ingredients or alternativeingredients, in order to find a better match. In one example, the CPUmay automatically identify one or more alternative ingredients that maybe used. Furthermore, CPU may automatically identify the one or morealternative ingredients by comparing the similarity of one or moremetrics of the simulant with metrics associated with the alternativeingredients, or with metrics of the compound.

At step S312, the CPU may output the weights of the ingredients to theuser. For example, the CPU may generate a three dimensional plot thatshows the metrics of the simulant and the compound. In addition, the CPUmay output the weights to an external device via the network 102.

At step S314, the simulant is formed based on the weights of theingredients determined at step S308. The simulant may be a packed powdermixture, a moldable plastic material, a solid material, gel or emulsion,or the like. In one example, the ingredients may be dry powdermaterials. In producing the simulant, a blend (according to thedetermined weights) of dry powder materials is placed into a custom-madetool and die set to form a mixture. Then, the mixture is compressed tofuse the materials together into a solid to reproduce the one or moremetrics of a solid compound (e.g. solid explosive threat). In anotherexample, the ingredients may be liquid materials. Then, a liquid mixture(according to the determined weights) of liquid materials may be formedto reproduce the one or more metrics of a liquid compound (e.g., liquidexplosive threat).

At step S316, the simulant is validated by direct measurement of the oneor more metrics, such as through the use of a variety of commerciallyavailable products for testing metrics such as density, Z_(eff), etc.

At step S318, the CPU may compare the measured one or more metrics ofthe simulant with the one or more metrics of the compound to determinewhether the metrics are within a predetermined threshold. If it isdetermined that the one or more metrics are within the predeterminedthreshold, then the process ends. However, if it is determined that theone or more metrics are not within the predetermined threshold, theprocess returns to step S302.

FIG. 4 is a schematic that shows a graphical user interface (GUI) 400according to one example. The GUI 400 may be a part of a website, webportal, personal computer application, or mobile application configuredto allow users to interact with the computer 100. The GUI 400 mayinclude a “Load Threats” button 402, a “Pick Threat” control 404, a“Load Ingredients” 406, a “Pick Ingredients” 408, a “Plot Points” button410, an “Optimize” button 412, a “Clear/Reset” button 414, a “Pickmetrics” control 416, and a display pane 418.

Upon activation of the “Load Threats” button 402, the CPU retrieves athreat list from the threat database 104. For example, the CPU mayretrieve the threat from the threat database 104 via the network 102.Upon activation of the “Pick Threat” control 404, the user may bepresented with a drop-down menu, search box, or other selection controlfor identifying the compound (e.g., threat) to be matched.

Upon activation of the “Load Ingredients” button 406, the CPU retrievesan ingredients list from the ingredient database 106. Upon activation ofthe “Pick Ingredients” control 408, the user may be presented with adrop-down menu, search box, or other selection control for identifyingthe one or more ingredients.

Upon activation of the “Plot Points” button 410, the CPU may generate athree dimensional plot. In addition, the CPU may identify using a changein attribute (e.g., color) of the one or more ingredients, the compound,and the simulant (optimized material).

Upon activation of the “Optimize” button 412, the CPU calculates theweight of each ingredient as previously described in step S208.

Upon activation of the “Clear/Reset” button 414, the CPU may clear theselected one or more ingredients, the compound, and the calculatedweights of each ingredients from the ingredient database 106.

Upon activation of the “Pick metrics” control 416, the user may bepresented with the available metrics. The user may then select one ormore metrics from the available metrics. The “display” pane 418 may showthe 3D plot that depicts the metrics of the selected ingredients, thecompound, and the simulant (optimized material). In one embodiment, the3D plot has data points that are selectable and movable, such as by auser drag-and-drop operation. In this instance, a user can adjust the 3Dplot to more closely conform to the compound metrics. The CPU may alsoidentify alternative ingredients that would conform to the metricsrepresented by the modified 3D plot and may present to the user theidentified alternative ingredients.

An additional “share” control (not shown), when selected, presents theuser with options to share (e.g., email, print) results and/orparameters with an external device.

To illustrate the capabilities of methodologies described herein,exemplary results are presented.

In one example, three properties are investigated: density, Z_(eff), andMAC that scaled perfectly with weight fraction. A test “threat” wascreated as a combination of three materials: 40% water, 40% corn syrup,and 20% ethanol. A test point was produced by multiplying the weights ofeach ingredient by the individual values of the three properties, andthen summing them.

The optimization was run with water, corn syrup, and ethanol selected asthe ingredients. The resulting formula was within ±0.005% of theoriginal percentages for all three ingredients. Additional investigationwas conducted to determine how these material properties scale withweight fraction. It was determined that some of these properties scalebetter than others. In response to such a determination, the optimizedparameters may be modified.

As a test of the computational modeling tool, a simulant for 70%hydrogen peroxide (HP) is formulated based on matching theoreticaldensity and Z_(eff) values. The original simulant (SDL1B) consisted of77.59% Karo light corn syrup, 20.46% distilled water, and 1.95%potassium meta-bisulfate, a combination of ingredient weights that waspredicted using the methodology described herein. The density wasmeasured with an Accupyc 1330 gas pycnometer from MicromeriticsInstrument Corporation and the X-ray properties were measured on aReveal CT80DR+EDS system from Leidos, Inc. The resulting data was thencompared with data for 70% HP that already had been acquired on thesesystems. The resulting data is shown in Table 1 and Table 2. The densityof the simulant was averaged over seven runs and the EDS measurementsfor the simulant were averaged over 15 scans. The SDL1B simulant wascompared against a commercial-off-the-shelf (COTS) simulant and found tobe better in only two of the five metrics as shown in Table 1 and Table2. The formula was re-optimized using the methodology described hereinand using the measured values (density and Z-effective) rather thantheoretical values for the threat (70% HP). The resulting simulant(SDL2B) consisted of 75.35% Karo light corn syrup, 22.47% distilledwater, and 2.18% potassium meta-bisulfate. All the differences of thefive metrics for SDL2B compared with the threat were less than 1.35%.The second simulant (SDL2B) surpassed the COTS simulant in four of thefive metrics measured.

TABLE 1 Experimental measurement data for 70% Hydrogen Peroxide andsimulants Density Density CT Z- CT Z- Lot Mean SD Effective EffectiveSample Number (g/cc) (g/cc) Mean SD 70% HP 14k26 1.2820 ±0.0010 7.645±0.035 HP 11 May 2015 1.2847 ±0.0005 7.601 ±0.028 Simulant SDL1BDifference — 0.21 — 0.58 — (%) HP 1 Jun. 2015 1.2803 ±0.0004 7.622±0.020 Simulant SDL2B Difference — 0.13 — 0.30 — (%) HP 22 Jan. 20151.2851 ±0.0004 7.617 ±0.025 Simulant COTS Difference — 0.24 — 0.37 — (%)

TABLE 2 Experimental measurement data for 70% Hydrogen Peroxide andsimulants High Energy Low Energy CTN CTN Lot CTN Mean CTN Mean CT SampleNumber Average SD Average SD Ratio 70% HP 14k26 12558.39 ±193.2612567.89 ±150.52 1.0008 HP 11May15 12748.06 ±185.91 12759.00 ±155.781.0009 Simulant SDL1B Difference — 1.51 — 1.52 — 0.01 (%) HP 1June1512696.94 ±192.06 12734.30 ±135.49 1.0029 Simulant SDL2B Difference —1.10 — 1.32 — 0.21 (%) HP 22Jan15 12389.36 ±207.53 12441.43 ±171.591.0042 Simulant COTS Difference — 1.35 — 1.01 — 0.34 (%)

Next, a hardware description of the computer 100 according to exemplaryembodiments is described. As discussed above, the computer 100 includesa CPU which performs the processes described herein, and the processdata and instructions may be stored in memory. These processes andinstructions may also be stored on a storage medium disk such as a harddrive (HDD) or portable storage medium or may be stored remotely.Further, the claimed advancements are not limited by the form of thecomputer-readable media on which the instructions of the inventiveprocess are stored. For example, the instructions may be stored on CDs,DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or anyother information processing device with which the computer 100communicates, such as a server or computer.

Further, the claimed advancements may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with the CPU and anoperating system such as Microsoft Windows 7, UNIX, Solaris, LINUX,Apple MAC-OS and other systems known to those skilled in the art.

In order to achieve the computer 100, the hardware elements may berealized by various circuitry elements, known to those skilled in theart. For example, the CPU may be a Xenon or Core processor from Intel ofAmerica or an Opteron processor from AMD of America, or may be otherprocessor types that would be recognized by one of ordinary skill in theart. Alternatively, the CPU may be implemented on an FPGA, ASIC, PLD orusing discrete logic circuits, as one of ordinary skill in the art wouldrecognize. Further, the CPU may also be implemented as multipleprocessors cooperatively working in parallel to perform the instructionsof the inventive processes described above.

The computer 100 can also include a network controller, such as an IntelEthernet PRO network interface card from Intel Corporation of America,for interfacing with network 102. As can be appreciated, the network 102can be a public network, such as the Internet, or a private network suchas LAN or WAN network, or any combination thereof and can also includePSTN or ISDN sub-networks. The network 102 can also be wired, such as anEthernet network, or can be wireless such as a cellular networkincluding EDGE, 3G and 4G wireless cellular systems. The wirelessnetwork can also be WiFi, Bluetooth, or any other wireless form ofcommunication that is known.

The computer 100 can further include a display controller, such as aNVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation ofAmerica, for interfacing with a display for implementing the GUIillustrated in FIG. 4. For example, the display may be a Hewlett PackardHPL2445w LCD monitor.

The exemplary circuit elements described in the context of the presentdisclosure may be replaced with other elements and structureddifferently than the examples provided herein.

The present disclosure is not limited to the specific circuit elementsdescribed herein. The present disclosure is also not limited to thespecific sizing and classification of these elements. For example, theskilled artisan will appreciate that the circuitry described herein maybe adapted based on changes on battery sizing and chemistry, or based onthe requirements of the intended back-up load to be powered.

In one embodiment, a data processing system may be configured to performthe algorithms shown in FIGS. 2 and 3. The data processing system mayinclude one or more processors and/or one or more heterogeneousprocessor systems.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, wherein the processorsare distributed across multiple components communicating in a network.The distributed components may include one or more client and servermachines, which may share processing in addition to various humaninterface and communication devices (e.g., display monitors, smartphones, tablets, personal digital assistants (PDAs)). The network may bea private network, such as a LAN or WAN, or may be a public network,such as the Internet. Input to the system may be received via directuser input or received remotely either in real-time or as a batchprocess. Additionally, some implementations may be performed on modulesor hardware not identical to those described. Accordingly, otherimplementations are within the scope that may be claimed.

The above-described hardware description is a non-limiting example ofcorresponding structure for performing the functionality describedherein.

The hardware description above, exemplified by any one of the structureexamples described above, constitutes or includes specializedcorresponding structure that is programmed or configured to perform thealgorithms shown in FIGS. 2 and 3.

A system which includes the features in the foregoing descriptionprovides numerous advantages to users. In particular, the system andassociated methodology identifies a simulant. The methodology describedherein could not be implemented by a human due to the sheer complexityof the process and calculations and includes a variety of novel featuresand elements that result in significantly more value than any construedabstract idea.

Numerous modifications and variations may be possible in light of theabove teachings. Therefore, it is to be understood that, within thescope of the appended claims, the invention may be practiced otherwisethan as specifically described herein.

Thus, the foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. As will be understood by thoseskilled in the art, the present invention may be embodied in otherspecific forms without departing from the spirit or essentialcharacteristics thereof. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting of the scopeof the invention or other claims. The disclosure, including any readilydiscernible variants of the teachings herein, defines, in part, thescope of the foregoing claim terminology such that no inventive subjectmatter is dedicated to the public.

1. A method for identifying a simulant, the method comprising: receivinga selection of a subject composition from a listing of subjectcompositions each being associated with one or more metrics havingtarget values; receiving a selection of at least three ingredients froma listing of simulant ingredients for the simulant, each simulantingredient of the listing of simulant ingredients being associated withone or more metrics and having an ingredient value; identifying forevaluation one or more metrics based on the selected subjectcomposition, the one or more metrics including at least one of physicalor chemical properties; determining proportions of each of the at leastthree ingredients by optimizing a quadratic function based on the one ormore metrics of the simulant; rendering, via a graphical user interface(GUI), a three-dimensional (3D) plot that depicts the one or moremetrics and a target point specified by the target values of theselected subject composition, the 3D plot having data points that areselectable and movable via the GUI to produce a new 3D plot representingmodified metrics of the simulant; when the target point specified by thetarget values of the selected subject composition is contained within aconvex set defined by the at least three ingredients, outputting thedetermined proportions of each of the at least three ingredients; andwhen the target point specified by the target values of the selectedsubject composition is not contained within a convex set defined by theat least three ingredients, receiving user input to adjust the convexset by user selecting and moving the data points of the 3D plot tomodify the metrics of the simulant to produce the new 3D plot,identifying alternative ingredients and/or alternative proportions forone or more of the at least three ingredients that more closely conformto the target values of the subject composition as represented by theuser adjusted convex set, the alternative ingredients and/or alternativeproportions representing the at least three ingredients that moreclosely conform to the target values of the subject composition, anddetermining proportions of each of the at least three ingredients thatmore closely conform to the target values of the subject composition byoptimizing the quadratic function based on the one or more metrics ofthe at least three ingredients of the simulant that more closely conformto the target values of the subject composition.
 2. The method of claim1, wherein the one or more metrics include at least one of physical orchemical properties selected from the group consisting of density,electron density, effective atomic number (Zeff), mass attenuationcoefficient (MAC), chemical compound ratio, Nitrogen-Oxygen toCarbon-Hydrogen ratio, dielectric constant, morphological property, andmillimeter wave reflectivity.
 3. The method of claim 2, wherein the 3Dplot has three axes corresponding to three metrics selected from thegroup consisting of density, electron density, effective atomic number(Z_(eff)), mass attenuation coefficient (MAC), chemical compound ratio,Nitrogen-Oxygen to Carbon-Hydrogen ratio, dielectric constant,morphological property, and millimeter wave reflectivity.
 4. The methodof claim 1, wherein the subject composition is an explosive, anexplosive precursor, or a flammable material, and wherein the one ormore metrics are density, effective atomic number, mass attenuationcoefficient, electron density, chemical compound ratio, dielectricconstant, or millimeter wave reflectivity.
 5. The method of claim 1,further comprising: determining differences between the target valuesand the ingredient values of the selected at least three ingredients;determining whether the differences are greater than a predeterminedthreshold; and identifying one or more alternative ingredients when thedifferences are greater than the predetermined threshold.
 6. The methodof claim 5, further comprising determining whether metrics associatedwith the alternative ingredients fall within the predeterminedthreshold.
 7. The method of claim 6, wherein the alternative ingredientsare automatically identified and proposed based on similarity of metricsof the alternative ingredients to metrics values associated with theselected one or more ingredients identified for evaluation.
 8. Themethod of claim 6, wherein the alternative ingredients are automaticallyidentified and proposed based on similarity of metrics of thealternative ingredients to the target values.
 9. A system foridentifying a simulant, the system comprising: one or more databasescontaining subject compositions and ingredients information, whereineach subject composition is associated with one or more metrics havingtarget values, and each ingredient is associated with one or moremetrics and having an ingredient value; and processing circuitryconfigured to: identify for evaluation one or more metrics based on asubject composition selected from a listing of subject compositions fromthe one or more databases as a selected subject composition, the one ormore metrics including at least one of physical or chemical properties;determine proportions of each of at least three ingredients selectedfrom a listing of ingredients for the simulant from the one or moredatabases, by optimizing a quadratic function based on one or moremetrics of the simulant; render a three-dimensional (3D) plot thatdepicts the one or more metrics and a target point specified by thetarget values of the selected subject composition, the 3D plot havingdata points that are selectable and movable to produce a new 3D plotrepresenting modified metrics of the simulant; when the target pointspecified by the target values of the selected subject composition iscontained within a convex set defined by the at least three ingredients,then output the determined proportions of each of the at least threeingredients; and when the target point specified by the target values ofthe selected subject composition is not contained within a convex setdefined by the at least three ingredients, identify alternativeingredients and/or alternative proportions for one or more of the atleast three ingredients that more closely conform to the target valuesof the subject composition as represented by an adjusted convex set,which is adjusted by selecting and moving the data points of the 3D plotto modify the metrics of the at least three ingredients of the simulantto produce the new 3D plot, the alternative ingredients and/oralternative proportions representing the at least three ingredients thatare modified to more closely conform to the target values of the subjectcomposition, and determine proportions of each of the at least threeingredients that are modified to more closely conform to the targetvalues of the subject composition by optimizing the quadratic functionbased on one or more metrics of the at least three ingredients of thesimulant that are modified to more closely conform to the target valuesof the subject composition.
 10. The system of claim 9, wherein the oneor more metrics include at least one of physical or chemical propertiesincluding at least one of density, electron density, effective atomicnumber (Zeff), mass attenuation coefficient (MAC), chemical compoundratio, Nitrogen-Oxygen to Carbon-Hydrogen ratio, dielectric constant,morphological property, and millimeter wave reflectivity.
 11. The systemof claim 10, wherein the 3D plot has three axes corresponding to threemetrics selected from the group consisting of density, electron density,effective atomic number (Z_(eff)), mass attenuation coefficient (MAC),chemical compound ratio, Nitrogen-Oxygen to Carbon-Hydrogen ratio,dielectric constant, morphological property, and millimeter wavereflectivity.
 12. The system of claim 9, wherein the subject compositionis an explosive, an explosive precursor, or a flammable material, andwherein the one or more metrics are density, effective atomic number,mass attenuation coefficient, electron density, chemical compound ratio,dielectric constant, or millimeter wave reflectivity.
 13. The system ofclaim 9, wherein the processing circuitry is further configured to:determine differences between the target values and the ingredientvalues of the selected at least three ingredients; determine whether thedifferences are greater than a predetermined threshold; and identify oneor more alternative ingredients when the differences are greater thanthe predetermined threshold.
 14. The system of claim 13, wherein theprocessing circuitry is further configured to determine whether metricsassociated with the alternative ingredients fall within thepredetermined threshold.
 15. The system of claim 14, wherein theprocessing circuitry is further configured to automatically identify andpropose alternative ingredients based on similarity of metrics of thealternative ingredients to the one or more metrics of the subjectcomposition.
 16. A method for identifying a simulant, the methodcomprising: identifying for evaluation one or more metrics based on asubject composition selected from a listing of subject compositions eachbeing associated with one or more metrics having target values, as aselected subject composition, the one or more metrics including at leastone of physical or chemical properties; determining proportions of eachof at least three ingredients selected from a listing of simulantingredients for the simulant, each simulant ingredient of the listing ofsimulant ingredients being associated with one or more metrics andhaving an ingredient value, by optimizing a quadratic function based onthe one or more metrics of the at least three ingredients of thesimulant; rendering a three-dimensional (3D) plot that depicts the oneor more metrics and a target point specified by the target values of theselected subject composition, the 3D plot having data points that areselectable and movable to produce a new 3D plot representing modifiedmetrics of the at least three ingredients of the simulant; when thetarget point specified by the target values of the selected subjectcomposition is contained within a convex set defined by the at leastthree ingredients, outputting the determined proportions of each of theat least three ingredients; and when the target point specified by thetarget values of the selected subject composition is not containedwithin a convex set defined by the at least three ingredients,identifying alternative ingredients and/or alternative proportions forone or more of at least three ingredients that are modified to moreclosely conform to the target values of the subject composition asrepresented by an adjusted convex set, which is adjusted by selectingand moving the data points of the 3D plot to modify the metrics of theat least three ingredients of the simulant to produce the new 3D plot,the alternative ingredients and/or alternative proportions representingthe at least three ingredients that are modified to more closely conformto the target values of the subject composition, and determiningproportions of each of the at least three ingredients by optimizing thequadratic function based on the one or more metrics of the at leastthree ingredients of the simulant that are modified to more closelyconform to the target values of the subject composition.
 17. The methodof claim 16, wherein the one or more metrics include at least one ofphysical or chemical properties including at least one of density,electron density, effective atomic number (Zeff), mass attenuationcoefficient (MAC), chemical compound ratio, Nitrogen-Oxygen toCarbon-Hydrogen ratio, dielectric constant, morphological property, andmillimeter wave reflectivity.
 18. The method of claim 17, wherein the 3Dplot has three axes corresponding to three metrics selected from thegroup consisting of density, electron density, effective atomic number(Z_(eff)), mass attenuation coefficient (MAC), chemical compound ratio,Nitrogen-Oxygen to Carbon-Hydrogen ratio, dielectric constant,morphological property, and millimeter wave reflectivity.
 19. The methodof claim 16, wherein the subject composition is an explosive, anexplosive precursor, or a flammable material, and wherein the one ormore metrics are density, effective atomic number, mass attenuationcoefficient, electron density, chemical compound ratio, dielectricconstant, or millimeter wave reflectivity.
 20. The method of claim 16,further comprising: determining differences between the target valuesand the ingredient values of the selected at least three ingredients;determining whether the differences are greater than a predeterminedthreshold; and identifying one or more alternative ingredients when thedifferences are greater than the predetermined threshold.
 21. The methodof claim 20, further comprising determining whether metrics associatedwith the alternative ingredients fall within the predeterminedthreshold.
 22. The method of claim 21, wherein the alternativeingredients are automatically identified and proposed based onsimilarity of metrics of the alternative ingredients to metrics valuesassociated with the selected one or more ingredients identified forevaluation.
 23. The method of claim 21, wherein the alternativeingredients are automatically identified and proposed based onsimilarity of metrics of the alternative ingredients to the targetvalues.